Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Abstract

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

References 175

1. 1.

   Ghassemi N., Shoeibi A., Rouhani M., Hosseini-Nejad H. Epileptic seizures detection in EEG signals using TQWT and ensemble learning. Proceedings of the 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE). 403-408.

   Not in our library

2. 2.

   Shoeibi A., Ghassemi N., Alizadehsani R., Rouhani M., Hosseini-Nejad H., Khosravi A., Panahiazar M., Nahavandi S. A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals. Expert Syst. Appl. 2021;163:113788.

   Not in our library10.1016/j.eswa.2020.113788

3. 3.

   Bhattacharyya A., Pachori R.B., Upadhyay A., Acharya U.R. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 2017;7:385.

   Not in our library10.3390/app7040385

4. 4.

   Kulaseharan S., Aminpour A., Ebrahimi M., Widjaja E. Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images. Clin. NeuroImage. 2019;21:101663.

   Not in our library10.1016/j.nicl.2019.101663

5. 5.

   Zazzaro G., Cuomo S., Martone A., Montaquila R.V., Toraldo G., Pavone L. Eeg signal analysis for epileptic seizures detection by applying data mining techniques. Internet Things. 2019;100048.

   Not in our library10.1016/j.iot.2019.03.002

6. 6.

   van Klink N., Mooij A., Huiskamp G., Ferrier C., Braun K., Hillebrand A., Zijlmans M. Simultaneous MEG and EEG to detect ripples in people with focal epilepsy. Clin. Neurophysiol. 2019;130:1175-1183.

   Not in our library10.1016/j.clinph.2019.01.027

7. 7.

   Pianou N., Chatziioannou S. Imaging with PET/CT in Patients with Epilepsy. Epilepsy Surgery and Intrinsic Brain Tumor Surgery. 2019;45-50.

   Not in our library

8. 8.

   Subasi A., Kevric J., Canbaz M.A. Epileptic seizure detection using hybrid machine learning methods. Neural Comput. Appl. 2019;31:317-325.

   Not in our library10.1007/s00521-017-3003-y

9. 9.

   Acharya U.R., Oh S.L., Hagiwara Y., Tan J.H., Adeli H., Subha D.P. Automated EEG-based screening of depression using deep convolutional neural network. Comput. Methods Programs Biomed. 2018;161:103-113.

   Not in our library10.1016/j.cmpb.2018.04.012

10. 10.

    Lauretani F., Longobucco Y., Ravazzoni G., Gallini E., Salvi M., Maggio M. Imaging the Functional Neuroanatomy of Parkinson’s Disease: Clinical Applications and Future Directions. Int. J. Environ. Res. Public Health. 2021;18:2356.

    Not in our library10.3390/ijerph18052356

11. 11.

    Carbó-Carreté M., Cañete-Massé C., Figueroa-Jiménez M.D., Peró-Cebollero M., Guàrdia-Olmos J. Relationship between Quality of Life and the Complexity of Default Mode Network in Resting State Functional Magnetic Resonance Image in Down Syndrome. Int. J. Environ. Res. Public Health. 2020;17:7127.

    Not in our library10.3390/ijerph17197127

12. 12.

    Morales Chacón L.M., González González J., Ríos Castillo M., Berrillo Batista S., Batista García-Ramo K., Santos Santos A., Cordero Quintanal N., Zaldívar Bermúdez M., Garbey Fernández R., Estupiñan Díaz B., et al. Surgical Outcome in Extratemporal Epilepsies Based on Multimodal Pre-Surgical Evaluation and Sequential Intraoperative Electrocorticography. Behav. Sci. 2021;11:30.

    Not in our library10.3390/bs11030030

13. 13.

    Takagi S., Sakuma S., Morita I., Sugimoto E., Yamaguchi Y., Higuchi N., Inamoto K., Ariji Y., Ariji E., Murakami H. Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre-and Post-Tooth Clenching Assessment. J. Clin. Med. 2020;9:3475.

    Not in our library10.3390/jcm9113475

14. 14.

    Ronan L., Alhusaini S., Scanlon C., Doherty C.P., Delanty N., Fitzsimons M. Widespread cortical morphologic changes in juvenile myoclonic epilepsy: Evidence from structural MRI. Epilepsia. 2012;53:651-658.

    Not in our library10.1111/j.1528-1167.2012.03413.x

15. 15.

    Sharma R., Pachori R.B. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst. Appl. 2015;42:1106-1117.

    Not in our library10.1016/j.eswa.2014.08.030

16. 16.

    Sheoran M., Kumar S., Chawla S. Methods of denoising of electroencephalogram signal: A review. Int. J. Biomed. Eng. Technol. 2015;18:385.

    Not in our library10.1504/ijbet.2015.071012

17. 17.

    Romaine J., Martín M.P., Ortiz J.S., Crespo J.M. EEG—Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction. Brain Sci. 2021;11:516.

    Not in our library10.3390/brainsci11040516

18. 18.

    Perez-Sanchez A.V., Perez-Ramirez C.A., Valtierra-Rodriguez M., Dominguez-Gonzalez A., Amezquita-Sanchez J.P. Wavelet Transform-Statistical Time Features-Based Methodology for Epileptic Seizure Prediction Using Electrocardiogram Signals. Mathematics. 2020;8:2125.

    Not in our library10.3390/math8122125

19. 19.

    Raschka S., Mirjalili V. Python Machine Learning: Machine Learning and Deep Learning with Python. Scikit-Learn and TensorFlow. 2017.

    Not in our library

20. 20.

    Bonaccorso G. Machine Learning Algorithms. 2017.

    Not in our library

21. 21.

    Gulli A., Pal S. Deep Learning with KERAS. 2017.

    Not in our library

22. 22.

    Tang X., Zhang X. Conditional adversarial domain adaptation neural network for motor imagery EEG decoding. Entropy. 2020;22:96.

    Not in our library10.3390/e22010096

23. 23.

    Alickovic E., Kevric J., Subasi A. Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed. Signal Process. Control. 2018;39:94-102.

    Not in our library10.1016/j.bspc.2017.07.022

24. 24.

    Sharma M., Bhurane A.A., Acharya U.R. MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection. Knowl. Based Syst. 2018;160:265-277.

    Not in our library10.1016/j.knosys.2018.07.019

25. 25.

    Mohammadpoor M., Shoeibi A., Shojaee H. A hierarchical classification method for breast tumor detection. Iran. J. Med. Phys. 2016;13:261-268.

    Not in our library

26. 26.

    Assi E.B., Nguyen D.K., Rihana S., Sawan M. Towards accurate prediction of epileptic seizures: A review. Biomed. Signal Process. Control. 2017;34:144-157.

    Not in our library10.1016/j.bspc.2017.02.001

27. 27.

    Khodatars M., Shoeibi A., Ghassemi N., Jafari M., Khadem A., Sadeghi D., Moridian P., Hussain S., Alizadehsani R., ZARE A., et al. Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review. arXiv. 2020.

    Not in our library10.1016/j.compbiomed.2021.104949

28. 28.

    Sadeghi D., Shoeibi A., Ghassemi N., Moridian P., Khadem A., Alizadehsani R., Teshnehlab M., Gorriz J.M., Nahavandi S. An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works. arXiv. 2021.

    Not in our library10.1016/j.compbiomed.2022.105554

29. 29.

    Craik A., He Y., Contreras-Vidal J.L. Deep learning for electroencephalogram (EEG) classification tasks: A review. J. Neural Eng. 2019;16:031001.

    Not in our library10.1088/1741-2552/ab0ab5

30. 30.

    Ghassemi N., Shoeibi A., Khodatars M., Heras J., Rahimi A., Zare A., Pachori R.B., Gorriz J.M. Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning. arXiv. 2021.

    Not in our library10.1016/j.asoc.2023.110511

31. 31.

    Sharifrazi D., Alizadehsani R., Hassannataj Joloudari J., Shamshirband S., Hussain S., Alizadeh Sani Z., Hasanzadeh F., Shoaibi A., Dehzangi A., Alinejad-Rokny H. CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering. Preprints. 2020;2020070650.

    Not in our library10.20944/preprints202007.0650.v1

32. 32.

    Srivastava N., Salakhutdinov R. Multimodal Learning with Deep Boltzmann Machines. NIPS. 2012;1:2.

    Not in our library

33. 33.

    Yu D., Deng L. Deep Learning and Its Applications to Signal and Information Processing Exploratory DSP. IEEE Signal Process. Mag. 2011;28:145-154.

    Not in our library10.1109/msp.2010.939038

34. 34.

    Ihle M., Feldwisch-Drentrup H., Teixeira C.A., Witon A., Schelter B., Timmer J., Schulze-Bonhage A. EPILEPSIAE–A European epilepsy database. Comput. Methods Programs Biomed. 2012;106:127-138.

    Not in our library10.1016/j.cmpb.2010.08.011

35. 35.

    Shoeb A.H. Application of Machine Learning to Epileptic Seizure onset Detection and Treatment. 2009.

    Not in our library

36. 36.

    Seizure Prediction Challenge.

    Not in our library

37. 37.

    Andrzejak R.G., Lehnertz K., Mormann F., Rieke C., David P., Elger C.E. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E. 2001;64:061907.

    Not in our library10.1103/physreve.64.061907

38. 38.

    Andrzejak R.G., Schindler K., Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of elec-troencephalographic recordings from epilepsy patients. Phys. Rev. E. 2012;86:046206.

    Not in our library10.1103/physreve.86.046206

39. 39.

    Stevenson N.J., Tapani K., Lauronen L., Vanhatalo S. A dataset of neonatal EEG recordings with seizure annotations. Sci. Data. 2019;6:1-8.

    Not in our library10.1038/sdata.2019.39

40. 40.

    Sharma R., Sircar P., Pachori R.B. Computer-aided diagnosis of epilepsy using bispectrum of EEG signals. Application of Biomedical Engineering in Neuroscience. 2019;197-220.

    Not in our library

41. 41.

    Goodfellow I., Bengio Y., Courville A., Bengio Y. Deep Learning. 2016;Volume 1:2.

    Not in our library

42. 42.

    Faust O., Hagiwara Y., Hong T.J., Lih O.S., Acharya U.R. Deep learning for healthcare applications based on physiological signals: A review. Comput. Methods Programs Biomed. 2018;161:1-13.

    Not in our library10.1016/j.cmpb.2018.04.005

43. 43.

    Yildirim O., Talo M., Ay B., Baloglu U.B., Aydin G., Acharya U.R. Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Comput. Biol. Med. 2019;113:103387.

    Not in our library10.1016/j.compbiomed.2019.103387

44. 44.

    Martis R.J., Acharya U.R., Lim C.M., Mandana K.M., Ray A.K., Chakraborty C. Application of higher order cumulant features for cardiac health diagnosis using ECG signals. Int. J. Neural Syst. 2013;23:1350014.

    Not in our library10.1142/s0129065713500147

45. 45.

    Pham T.-H., Vicnesh J., Wei J.K.E., Oh S.L., Arunkumar N., Abdulhay E.W., Ciaccio E.J., Acharya U.R. Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals. Int. J. Environ. Res. Public Health. 2020;17:971.

    Not in our library10.3390/ijerph17030971

46. 46.

    Alizadehsani R., Roshanzamir M., Hussain S., Khosravi A., Koohestani A., Zangooei M.H., Abdar M., Beykikhoshk A., Shoeibi A., Zare A., et al. Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020). Ann. Oper. Res. 2021;1-42.

    Not in our library10.1007/s10479-021-04006-2

47. 47.

    Alizadehsani R., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Gorriz J.M., Hussain S., Arco J.E., Sani Z.A., Khozeimeh F., et al. Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data. arXiv. 2021.

    Not in our library

48. 48.

    Shoeibi A., Khodatars M., Alizadehsani R., Ghassemi N., Jafari M., Moridian P., Khadem A., Sadehi D., Hussain S., Zare A., et al. Automated detection and forecasting of covid-19 using deep learning techniques: A review. arXiv. 2020.

    Not in our library

49. 49.

    Krizhevsky A., Sutskever I., Hinton G.E. Imagenet classification with deep convolutional neural networks. Commun. ACM. 2012;60:1097-1105.

    Not in our library10.1145/3065386

50. 50.

    Avcu M.T., Zhang Z., Chan D.W.S. Seizure detection using least eeg channels by deep convolutional neural network. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing. 1120-1124.

    Not in our library

51. 51.

    Hossain M.S., Amin S.U., Alsulaiman M., Muhammad G. Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization. ACM Trans. Multim. Comput. Commun. Appl. 2019;15:1-17.

    Not in our library

52. 52.
    Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network · 2019

    Not checked
53. 53.

    Asif U., Roy S., Tang J., Harrer S. SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-Oncology. 2020;77-87.

    Not in our library

54. 54.

    Covert I.C., Krishnan B., Najm I., Zhan J., Shore M., Hixson J., Po M.J. Temporal graph convolutional networks for automatic seizure detection. Proceedings of the Machine Learning for Healthcare Conference. 160-180.

    Not in our library

55. 55.

    Bouaziz B., Chaari L., Batatia H., Quintero-Rincón A. Epileptic seizure detection using a convolutional neural network. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. 2019;79-86.

    Not in our library

56. 56.

    Deng J., Dong W., Socher R., Li L.J., Li K., Li F.F. Imagenet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. 248-255.

    Not in our library

57. 57.

    Taqi A.M., Al-Azzo F., Mariofanna M., Al-Saadi J.M. Classification and discrimination of focal and non-focal EEG signals based on deep neural network. Proceedings of the 2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT). 86-92.

    Not in our library

58. 58.

    Bizopoulos P., Lambrou G.I., Koutsouris D. Signal2image modules in deep neural networks for eeg classification. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 702-705.

    Not in our library10.1109/embc.2019.8856620

59. 59.

    Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv. 2014.

    Not in our library

60. 60.

    Ahmedt-Aristizabal D., Fookes C., Nguyen K., Denman S., Sridharan S., Dionisio S. Deep facial analysis: A new phase I epilepsy evaluation using computer vision. Epilepsy Behav. 2018;82:17-24.

    Not in our library10.1016/j.yebeh.2018.02.010

61. 61.

    Emami A., Kunii N., Matsuo T., Shinozaki T., Kawai K., Takahashi H. Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. NeuroImage Clin. 2019;22:101684.

    Not in our library10.1016/j.nicl.2019.101684

62. 62.

    Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1-9.

    Not in our library

63. 63.

    Ayoobi N., Sharifrazi D., Alizadehsani R., Shoeibi A., Gorriz J.M., Moosaei H., Khosravi H., Nahavandi S., Chofreh A.G., Goni F.A. Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods. arXiv. 2021.

    Not in our library10.1016/j.rinp.2021.104495

64. 64.

    Antoniades A., Spyrou L., Took C.C., Sanei S. Deep learning for epileptic intracranial EEG data. Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). 1-6.

    Not in our library

65. 65.

    Achilles F., Tombari F., Belagiannis V., Loesch A.M., Noachtar S., Navab N. Convolutional neural networks for real-time epileptic seizure detection. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 2016;6:264-269.

    Not in our library10.1080/21681163.2016.1141062

66. 66.

    Park C., Choi G., Kim J., Kim S., Kim T.J., Min K., Jung K.-Y., Chong J. Epileptic seizure detection for multi-channel EEG with deep convolutional neural network. Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC). 1-5.

    Not in our library

67. 67.

    Tjepkema-Cloostermans M.C., de Carvalho R.C., van Putten M.J. Deep learning for detection of focal epileptiform discharges from scalp EEG recordings. Clin. Neurophysiol. 2018;129:2191-2196.

    Not in our library10.1016/j.clinph.2018.06.024

68. 68.

    Golmohammadi M., Ziyabari S., Shah V., de Diego S.L., Obeid I., Picone J. Deep architectures for automated seizure detection in scalp EEGs. arXiv. 2017.

    Not in our library

69. 69.

    Roy S., Kiral-Kornek I., Harrer S. Deep learning enabled automatic abnormal EEG identification. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2756-2759.

    Not in our library10.1109/embc.2018.8512756

70. 70.

    Nejedly P., Kremen V., Sladky V., Nasseri M., Guragain H., Klimes P., Cimbalnik J., Varatharajah Y., Brinkmann B.H., Worrell G.A. Deep-learning for seizure forecasting in canines with epilepsy. J. Neural Eng. 2019;16:036031.

    Not in our library10.1088/1741-2552/ab172d

71. 71.

    Iešmantas T., Alzbutas R. Convolutional neural network for detection and classification of seizures in clinical data. Med. Biol. Eng. Comput. 2020;58:1919-1932.

    Not in our library10.1007/s11517-020-02208-7

72. 72.

    San-Segundo R., Gil-Martín M., D’Haro-Enríquez L.F., Pardo J.M. Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. Comput. Biol. Med. 2019;109:148-158.

    Not in our library10.1016/j.compbiomed.2019.04.031

73. 73.

    Sui L., Zhao X., Zhao Q., Tanaka T., Cao J. Localization of Epileptic Foci by Using Convolutional Neural Network Based on iEEG. IFIP International Conference on Artificial Intelligence Applications and Innovations. 2019;331-339.

    Not in our library

74. 74.

    Akut R. Wavelet based deep learning approach for epilepsy detection. Health Inf. Sci. Syst. 2019;7:1-9.

    Not in our library10.1007/s13755-019-0069-1

75. 75.

    Türk Ö., Özerdem M.S. Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals. Brain Sci. 2019;9:115.

    Not in our library10.3390/brainsci9050115

76. 76.

    Liu J., Woodson B. Deep learning classification for epilepsy detection using a single channel electroencephalography (EEG). Proceedings of the 2019 3rd International Conference on Deep Learning Technologies. 23-26.

    Not in our library

77. 77.

    Tian X., Deng Z., Ying W., Choi K.-S., Wu D., Qin B., Wang J., Shen H., Wang S. Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection. IEEE Trans. Neural Syst. Rehabil. Eng. 2019;27:1962-1972.

    Not in our library10.1109/tnsre.2019.2940485

78. 78.

    Ansari A.H., Cherian P.J., Caicedo A., Naulaers G., De Vos M., Van Huffel S. Neonatal Seizure Detection Using Deep Convolutional Neural Networks. Int. J. Neural Syst. 2019;29:1850011.

    Not in our library10.1142/s0129065718500119

79. 79.

    Cao J., Zhu J., Hu W., Kummert A. Epileptic Signal Classification with Deep EEG Features by Stacked CNNs. IEEE Trans. Cogn. Dev. Syst. 2020;12:709-722.

    Not in our library10.1109/tcds.2019.2936441

80. 80.

    Thomas J., Comoretto L., Jin J., Dauwels J., Cash S.S., Westover M.B. EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018. 3148-3151.

    Not in our library10.1109/embc.2018.8512930

81. 81.

    Boonyakitanont P., Lek-uthai A., Chomtho K., Songsiri J. A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals. bioRxiv. 2019;702654.

    Not in our library10.1101/702654

82. 82.

    Daoud H.G., Abdelhameed A.M., Bayoumi M. Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network. Proceedings of the 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA). 2018;182-186.

    Not in our library

83. 83.

    Craley J., Johnson E., Venkataraman A. Integrating convolutional neural networks and probabilistic graphical modeling for epileptic seizure detection in multichannel EEG. International Conference on Information Processing in Medical Imaging. 2019;291-303.

    Not in our library

84. 84.

    Jaoude M.A., Jing J., Sun H., Jacobs C.S., Pellerin K.R., Westover M.B., Cash S.S., Lam A.D. Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning. Clin. Neurophysiol. 2020;131:133-141.

    Not in our library10.1016/j.clinph.2019.09.031

85. 85.

    Zhang J., Wu H., Su W., Wang X., Yang M., Wu J. A New Approach for Classification of Epilepsy EEG Signals Based on Temporal Convolutional Neural Networks. Proceedings of the 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 8–9 December 2018. Volume 2;80-84.

    Not in our library

86. 86.

    Ullah I., Hussain M., Qazi E.-U.-H., Aboalsamh H. An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 2018;107:61-71.

    Not in our library10.1016/j.eswa.2018.04.021

87. 87.

    Acharya U.R., Oh S.L., Hagiwara Y., Tan J.H., Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 2018;100:270-278.

    Not in our library10.1016/j.compbiomed.2017.09.017

88. 88.

    Page A., Shea C., Mohsenin T. Wearable seizure detection using convolutional neural networks with transfer learning. Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS). 1086-1089.

    Not in our library

89. 89.

    O’Shea A., Lightbody G., Boylan G., Temko A. Neonatal seizure detection using convolutional neural networks. Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). 1-6.

    Not in our library

90. 90.

    Johansen A.R., Jin J., Maszczyk T., Dauwels J., Cash S.S., Westover M.B. Epileptiform spike detection via convolutional neural networks. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 754-758.

    Not in our library10.1109/icassp.2016.7471776

91. 91.

    Yıldırım Ö., Baloglu U.B., Acharya U.R. A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. Appl. 2018;32:1-12.

    Not in our library10.1007/s00521-018-3889-z

92. 92.

    Chen X., Ji J., Ji T., Li P. Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Washington, DC, USA, 2–4 May 2018. 226-235.

    Not in our library

93. 93.

    Yao X., Cheng Q., Zhang G.Q. A novel independent RNN approach to classification of seizures against non-seizures. arXiv. 2019.

    Not in our library

94. 94.

    Lu D., Triesch J. Residual deep convolutional neural network for eeg signal classification in epilepsy. arXiv. 2019.

    Not in our library

95. 95.

    Wei Z., Zou J., Zhang J., Xu J. Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomed. Signal Process. Control. 2019;53:101551.

    Not in our library10.1016/j.bspc.2019.04.028

96. 96.

    Meisel C., Atrache R.E., Jackson M., Schubach S., Ufongene C., Loddenkemper T. Deep learning from wristband sensor data: Towards wearable, non-invasive seizure forecasting. arXiv. 2019.

    Not in our library10.1111/epi.16719

97. 97.

    Yuvaraj R., Thomas J., Kluge T., Dauwels J. A deep Learning Scheme for Automatic Seizure Detection from Long-Term Scalp EEG. Proceedings of the 2018 52nd Asilomar Conference on Signals. 368-372.

    Not in our library

98. 98.

    Fukumori K., Nguyen H.T.T., Yoshida N., Tanaka T. Fully Data-driven Convolutional Filters with Deep Learning Models for Epileptic Spike Detection. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2772-2776.

    Not in our library

99. 99.

    Zhao X., Solé-Casals J., Li B., Huang Z., Wang A., Cao J., Tanaka T., Zhao Q. Classification of Epileptic IEEG Signals by CNN and Data Augmentation. Proceedings of the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 926-930.

    Not in our library

100. 100.

     Abiyev R., Arslan M., Idoko J.B., Sekeroglu B., Ilhan A. Identification of epileptic eeg signals using convolutional neural networks. Appl. Sci. 2020;10:4089.

     Not in our library10.3390/app10124089

101. 101.

     Lin L.-C., Ouyang C.-S., Wu R.-C., Yang R.-C., Chiang C.-T. Alternative Diagnosis of Epilepsy in Children without Epileptiform Discharges Using Deep Convolutional Neural Networks. Int. J. Neural Syst. 2019;30:1850060.

     Not in our library10.1142/s0129065718500600

102. 102.

     Yao X., Cheng Q., Zhang G.Q. Automated Classification of Seizures against Nonseizures: A Deep Learning Approach. arXiv. 2019.

     Not in our library

103. 103.

     Talathi S.S. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems. arXiv. 2017.

     Not in our library

104. 104.

     Roy S., Kiral-Kornek I., Harrer S. ChronoNet: A deep recurrent neural network for abnormal EEG identification. Conference on Artificial Intelligence in Medicine in Europe. 2019;47-56.

     Not in our library

105. 105.

     Vidyaratne L., Glandon A., Alam M., Iftekharuddin K.M. Deep recurrent neural network for seizure detection. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN). 1202-1207.

     Not in our library

106. 106.

     Hussein R., Palangi H., Ward R., Wang Z.J. Epileptic seizure detection: A deep learning approach. arXiv. 2018.

     Not in our library

107. 107.

     Ahmedt-Aristizabal D., Fookes C., Nguyen K., Sridharan S. Deep Classification of Epileptic Signals. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 332-335.

     Not in our library10.1109/embc.2018.8512249

108. 108.

     Hussein R., Palangi H., Ward R.K., Wang Z.J. Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals. Clin. Neurophysiol. 2019;130:25-37.

     Not in our library10.1016/j.clinph.2018.10.010

109. 109.

     Jaafar S.T., Mohammadi M. Epileptic Seizure Detection using Deep Learning Approach. UHD J. Sci. Technol. 2019;3:41-50.

     Not in our library10.21928/uhdjst.v3n2y2019.pp41-50

110. 110.

     Hussein R., Palangi H., Wang Z.J., Ward R. Robust detection of epileptic seizures using deep neural networks. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2546-2550.

     Not in our library

111. 111.

     Verma A., Janghel R.R. Epileptic Seizure Detection Using Deep Recurrent Neural Networks in EEG Signals. Advances in Biomedical Engineering and Technology. 2021;189-198.

     Not in our library

112. 112.

     Geng M., Zhou W., Liu G., Li C., Zhang Y. Epileptic Seizure Detection Based on Stockwell Transform and Bidirectional Long Short-Term Memory. IEEE Trans. Neural Syst. Rehabil. Eng. 2020;28:573-580.

     Not in our library10.1109/tnsre.2020.2966290

113. 113.

     Rajaguru H., Prabhakar S.K. Multilayer Autoencoders and EM-PCA with Genetic Algorithm for Epilepsy Classification from EEG. Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 29–31 March 2018. 2018;353-358.

     Not in our library

114. 114.

     Sharathappriyaa V., Gautham S., Lavanya R. Auto-encoder Based Automated Epilepsy Diagnosis. Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 19–22 September 2018. 2018;976-982.

     Not in our library

115. 115.

     Qiu Y., Zhou W., Yu N., Du P. Denoising Sparse Autoencoder Based Ictal EEG Classification. IEEE Trans. Neural Syst. Rehabil. Eng. 2018;26:1717-1726.

     Not in our library10.1109/tnsre.2018.2864306

116. 116.
     Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures · 2019

     Not checked
117. 117.

     Yan B., Wang Y., Li Y., Gong Y., Guan L., Yu S. An EEG signal classification method based on sparse auto-encoders and support vector machine. Proceedings of the 2016 IEEE/CIC International Conference on Communications in China (ICCC). 1-6.

     Not in our library

118. 118.

     Yuan Y., Xun G., Suo Q., Jia K., Zhang A. Wave2Vec: Deep representation learning for clinical temporal data. Neurocomputing. 2019;324:31-42.

     Not in our library10.1016/j.neucom.2018.03.074

119. 119.

     Yuan Y., Xun G., Ma F., Suo Q., Xue H., Jia K., Zhang A. A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics, Las Vegas, NV, USA, 4–7 March 2018. 2018;206-209.

     Not in our library

120. 120.

     Emami A., Kunii N., Matsuo T., Shinozaki T., Kawai K., Takahashi H. Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system. Comput. Biol. Med. 2019;110:227-233.

     Not in our library10.1016/j.compbiomed.2019.05.025

121. 121.

     Yuan Y., Xun G., Jia K., Zhang A. A multi-view deep learning method for epileptic seizure detection using short-time fourier transform. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 213-222.

     Not in our library

122. 122.

     Lin Q., Ye S.Q., Huang X.M., Li S.Y., Zhang M.Z., Xue Y., Chen W.S. Classification of epileptic EEG signals with stacked sparse autoencoder based on deep learning. International Conference on Intelligent Computing. 2016;802-810.

     Not in our library

123. 123.

     Gasparini S., Campolo M., Ieracitano C., Mammone N., Ferlazzo E., Sueri C., Tripodi G.G., Aguglia U., Morabito F.C. Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures. Entropy. 2018;20:43.

     Not in our library10.3390/e20020043

124. 124.

     Karim A.M., Güzel M.S., Tolun M.R., Kaya H., Çelebi F.V. A new generalized deep learning framework combining sparse autoencoder and Taguchi method for novel data classification and processing. Math. Probl. Eng. 2018.

     Not in our library10.1155/2018/3145947

125. 125.

     Karim A.M., Güzel M.S., Tolun M.R., Kaya H., Çelebi F.V. A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing. Biocybern. Biomed. Eng. 2019;39:148-159.

     Not in our library10.1016/j.bbe.2018.11.004

126. 126.

     Karim A.M., Karal Ö., Çelebi F.V. A new automatic epilepsy serious detection method by using deep learning based on discrete wavelet transform. 2018;Volume 4:15-18.

     Not in our library

127. 127.

     Hosseini M.P., Soltanian-Zadeh H., Elisevich K., Pompili D. Cloud-based deep learning of big EEG data for epileptic seizure prediction. Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). 1151-1155.

     Not in our library

128. 128.

     Singh K., Malhotra J. Stacked autoencoders based deep learning approach for automatic epileptic seizure detection. Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). 249-254.

     Not in our library

129. 129.

     Le T.X., Le T.T., Dinh V.V., Tran Q.L., Nguyen L.T., Nguyen D.T. Deep learning for epileptic spike detection. VNU J. Sci. Comput. Sci. Commun. Eng. 2018;33:1-13.

     Not in our library

130. 130.

     Turner J.T., Page A., Mohsenin T., Oates T. Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. arXiv. 2017.

     Not in our library

131. 131.

     Fang Z., Leung H., Choy C.S. Spatial temporal GRU convnets for vision-based real time epileptic seizure detection. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 1026-1029.

     Not in our library

132. 132.

     Choi G., Park C., Kim J., Cho K., Kim T.-J., Bae H., Min K., Jung K.-Y., Chong J. A Novel Multi-scale 3D CNN with Deep Neural Network for Epileptic Seizure Detection. Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE). 1-2.

     Not in our library

133. 133.

     Thodoroff P., Pineau J., Lim A. Learning robust features using deep learning for automatic seizure detection. Mach. Learn. Healthc. Conf. 2016;56:178-190.

     Not in our library

134. 134.

     Liang W., Pei H., Cai Q., Wang Y. Scalp EEG epileptogenic zone recognition and localization based on long-term recurrent convolutional network. Neurocomputing. 2020;396:569-576.

     Not in our library10.1016/j.neucom.2018.10.108

135. 135.

     RaviPrakash H., Korostenskaja M., Castillo E.M., Lee K.H., Salinas C.M., Baumgartner J., Anwar S.M., Spampinato C., Bagci U. Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery. Front. Neurosci. 2020;14:409.

     Not in our library10.3389/fnins.2020.00409

136. 136.

     Yuan Y., Xun G., Jia K., Zhang A. A Multi-View Deep Learning Framework for EEG Seizure Detection. IEEE J. Biomed. Health Inform. 2018;23:83-94.

     Not in our library10.1109/jbhi.2018.2871678

137. 137.
     FusionAtt: Deep Fusional Attention Networks for Multi-Channel Biomedical Signals · 2019

     Not checked
138. 138.

     Wen T., Zhang Z. Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals. IEEE Access. 2018;6:25399-25410.

     Not in our library10.1109/access.2018.2833746

139. 139.

     Abdelhameed A.M., Daoud H.G., Bayoumi M. Epileptic Seizure Detection using Deep Convolutional Autoencoder. Proceedings of the 2018 IEEE International Workshop on Signal Processing Systems (SiPS), Los Alamitos, CA, USA, 21–24 October 2018. 2018;223-228.

     Not in our library

140. 140.

     Antoniades A., Spyrou L., Martin-Lopez D., Valentin A., Alarcon G., Sanei S., Took C.C. Deep neural architectures for mapping scalp to intracranial EEG. Int. J. Neural Syst. 2018;28:1850009.

     Not in our library10.1142/s0129065718500090

141. 141.

     Dev K.B., Jogi P.S., Niyas S., Vinayagamani S., Kesavadas C., Rajan J. Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network. Biomed. Signal Process. Control. 2019;52:218-225.

     Not in our library

142. 142.

     Gill R.S., Hong S.J., Fadaie F., Caldairou B., Bernhardt B.C., Barba C., Brandt A., Coelho V.C., d’Incerti L., Lenge M. Deep convolutional networks for automated detection of epileptogenic brain malformations. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 490-497.

     Not in our library

143. 143.

     Hao Y., Khoo H.M., von Ellenrieder N., Zazubovits N., Gotman J. DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning. NeuroImage Clin. 2018;17:962-975.

     Not in our library10.1016/j.nicl.2017.12.005

144. 144.

     Hosseini M.P., Tran T.X., Pompili D., Elisevich K., Soltanian-Zadeh H. Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fMRI and EEG big data. Proceedings of the 2017 IEEE international conference on autonomic computing (ICAC). 83-92.

     Not in our library

145. 145.

     Yan M., Liu L., Chen S., Pan Y. A deep learning method for prediction of benign epilepsy with centrotemporal spikes. International Symposium on Bioinformatics Research and Applications. 2018;253-258.

     Not in our library

146. 146.

     Gleichgerrcht E., Munsell B., Bhatia S., Vandergrift W.A., Rorden C., McDonald C., Edwards J., Kuzniecy R., Bonilha L. Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery. Epilepsia. 2018;59:1643-1654.

     Not in our library10.1111/epi.14528

147. 147.

     Jiang H., Gao F., Duan X., Bai Z., Wang Z., Ma X., Chen Y.W. Transfer Learning and Fusion Model for Classification of Epileptic PET Images. Innovation in Medicine and Healthcare Systems, and Multimedia. 2019;71-79.

     Not in our library

148. 148.

     Shiri I., Ghafarian P., Geramifar P., Leung K.H.-Y., Ghelichoghli M., Oveisi M., Rahmim A., Ay M.R. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC). Eur. Radiol. 2019;29:6867-6879.

     Not in our library10.1007/s00330-019-06229-1

149. 149.

     Rosas-Romero R., Guevara E., Peng K., Nguyen D.K., Lesage F., Pouliot P., Lima-Saad W.E. Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals. Comput. Biol. Med. 2019;111:103355.

     Not in our library10.1016/j.compbiomed.2019.103355

150. 150.
     Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System · 2018

     Not checked
151. 151.

     Alizadehsani R., Khosravi A., Roshanzamir M., Abdar M., Sarrafzadegan N., Shafie D., Khozeimeh F., Shoeibi A., Nahavandi S., Panahiazar M., et al. Coronary Artery Disease Detection Using Artificial Intelligence Techniques: A Survey of Trends, Geographical Differences and Diagnostic Features 1991–2020. Comput. Biol. Med. 2020;128:104095.

     Not in our library10.1016/j.compbiomed.2020.104095

152. 152.

     Khozeimeh F., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Alizadehsani R., Gorriz J.M., Hussain S., Sani Z.A., Moosaei H. CNN AE: Convolution Neural Network combined with Autoencoder approach to detect survival chance of COVID 19 patients. arXiv. 2021.

     Not in our library10.1038/s41598-021-93543-8

153. 153.

     Ghassemi N., Shoeibi A., Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed. Signal Process. Control. 2020;57:101678.

     Not in our library10.1016/j.bspc.2019.101678

154. 154.

     Ghassemi N., Mahami H., Darbandi M.T., Shoeibi A., Hussain S., Nasirzadeh F., Alizadehsani R., Nahabandi D., Khosravi A., Nahavandi S. Material Recognition for Automated Progress Monitoring using Deep Learning Methods. arXiv. 2020.

     Not in our library

155. 155.

     Yildirim O., Baloglu U.B., Acharya U.R. A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals. Int. J. Environ. Res. Public Health. 2019;16:599.

     Not in our library10.3390/ijerph16040599

156. 156.

     Kim S., Kim J., Chun H.-W. Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease. Int. J. Environ. Res. Public Health. 2018;15:1750.

     Not in our library10.3390/ijerph15081750

157. 157.

     Sarić R., Jokić D., Beganović N., Pokvić L.G., Badnjević A. FPGA-based real-time epileptic seizure classification using Artificial Neural Network. Biomed. Signal Process. Control. 2020;62:102106.

     Not in our library10.1016/j.bspc.2020.102106

158. 158.

     Saidi A., Othman S.B., Kacem W., Saoud S.B. FPGA Implementation of EEG Signal Analysis System for the Detection of epileptic seizure. Proceedings of the 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET). 415-420.

     Not in our library

159. 159.

     Feng L., Li Z., Wang Y. VLSI Design of SVM-Based Seizure Detection System with On-Chip Learning Capability. IEEE Trans. Biomed. Circuits Syst. 2017;12:171-181.

     Not in our library10.1109/tbcas.2017.2762721

160. 160.

     Craley J., Johnson E., Jouny C., Venkataraman A. Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks. Biomed. Signal Process. Control. 2021;64:102360.

     Not in our library10.1016/j.bspc.2020.102360

161. 161.

     Martínez-Rodrigo A., García-Martínez B., Huerta Álvaro, Alcaraz R. Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network. Sensors. 2021;21:3050.

     Not in our library10.3390/s21093050

162. 162.

     Moore J.L., Carvalho D.Z., Louis E.K.S., Bazil C. Sleep and epilepsy: A focused review of pathophysiology, clinical syndromes, co-morbidities and therapy. Neurotherapeutics. 2021;18:1-11.

     Not in our library10.1007/s13311-021-01021-w

163. 163.

     Shoeibi A., Khodatars M., Jafari M., Moridian P., Rezaei M., Alizadehsani R., Khozeimeh F., Gorriz J.M., Heras J., Acharya U.R., et al. Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review. arXiv. 2021.

     Not in our library10.1016/j.compbiomed.2021.104697

164. 164.

     Rim B., Sung N.J., Min S., Hong M. Deep learning in physiological signal data: A survey. Sensors. 2020;20:969.

     Not in our library10.3390/s20040969

165. 165.

     LeCun Y. 1.1 deep learning hardware: Past present and future. Proceedings of the 2019 IEEE International Solid-State Circuits Conference-(ISSCC). 12-19.

     Not in our library

166. 166.

     Haensch W., Gokmen T., Puri R. The next generation of deep learning hardware: Analog computing. Proc. IEEE. 2018;107:108-122.

     Not in our library10.1109/jproc.2018.2871057

167. 167.

     Tzallas A.T., Tsipouras M.G., Tsalikakis D.G., Karvounis E.C., Astrakas L., Konitsiotis S., Tzaphlidou M. Automated epileptic seizure detection methods: A review study. Epilepsy-Histol. Electroencephalogr. Psychol. Asp. 2012;4:75-98.

     Not in our library

168. 168.

     Paul Y. Various epileptic seizure detection techniques using biomedical signals: A review. Brain Inform. 2018;5:1-19.

     Not in our library10.1186/s40708-018-0084-z

169. 169.
     A review of epileptic seizure detection using machine learning classifiers · 2020

     No notice
170. 170.

     Boonyakitanont P., Lek-Uthai A., Chomtho K., Songsiri J. A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed. Signal Process. Control. 2020;57:101702.

     Not in our library10.1016/j.bspc.2019.101702

171. 171.

     Chakrabarti S., Swetapadma A., Pattnaik P.K. A review on epileptic seizure detection and prediction using soft computing techniques. Smart Techniques for a Smarter Planet. 2019;37-51.

     Not in our library

172. 172.

     Rajendran T., Sridhar K.P. An overview of EEG seizure detection units and identifying their complexity-A review. Curr. Signal Transduct. Ther. 2020;15:234-242.

     Not in our library10.2174/1574362413666181030103616

173. 173.

     Rasheed K., Qayyum A., Qadir J., Sivathamboo S., Kwan P., Kuhlmann L., O’Brien T., Razi A. Machine learning for predicting epileptic seizures using eeg signals: A review. IEEE Rev. Biomed. Eng. 2020;14:139-155.

     Not in our library10.1109/rbme.2020.3008792

174. 174.

     Abbasi B., Goldenholz D.M. Machine learning applications in epilepsy. Epilepsia. 2019;60:2037-2047.

     Not in our library10.1111/epi.16333

175. 175.

     Acharya U.R., Hagiwara Y., Adeli H. Automated seizure prediction. Epilepsy Behav. 2018;88:251-261.

     Not in our library10.1016/j.yebeh.2018.09.030

Cited by papers we hold 1

• Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures · 2021
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